1. Introduction

2. Materials and methods

3. Results and discussion

   3.1. Exploratory data analysis

   3.2. Modeling

4. Conclusion

• Data from the Vaccine Adverse Event Reporting System (VAERS) created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC)

• Contains 3 datasets:

  1. PATIENTS.CSV

  2. VACCINES.CSV

  3. SYMPTOMS.CSV

• Datasets connected by patient IDs (VAERS_ID)

PATIENTS.CSV: Contains information about the individuals that received the vaccines

## # A tibble: 34,121 x 35
##   VAERS_ID RECVDATE  STATE AGE_YRS CAGE_YR CAGE_MO SEX   RPT_DATE   SYMPTOM_TEXT
##   <chr>    <chr>     <chr>   <dbl>   <dbl>   <dbl> <chr> <date>     <chr>       
## 1 0916600  01/01/20… TX         33      33      NA F     NA         "Right side…
## 2 0916601  01/01/20… CA         73      73      NA F     NA         "Approximat…
## 3 0916602  01/01/20… WA         23      23      NA F     NA         "About 15 m…
## # … with 34,118 more rows, and 26 more variables: DIED <chr>, DATEDIED <chr>,
## #   L_THREAT <chr>, ER_VISIT <chr>, HOSPITAL <chr>, HOSPDAYS <dbl>,
## #   X_STAY <chr>, DISABLE <chr>, RECOVD <chr>, VAX_DATE <chr>,
## #   ONSET_DATE <chr>, NUMDAYS <dbl>, LAB_DATA <chr>, V_ADMINBY <chr>,
## #   V_FUNDBY <chr>, OTHER_MEDS <chr>, CUR_ILL <chr>, HISTORY <chr>,
## #   PRIOR_VAX <chr>, SPLTTYPE <chr>, FORM_VERS <dbl>, TODAYS_DATE <chr>,
## #   BIRTH_DEFECT <chr>, OFC_VISIT <chr>, ER_ED_VISIT <chr>, ALLERGIES <chr>

VACCINES.CSV: Contains information about the received vaccine

## # A tibble: 34,630 x 8
##    VAERS_ID VAX_TYPE VAX_MANU         VAX_LOT VAX_DOSE_SERIES VAX_ROUTE VAX_SITE
##    <chr>    <chr>    <chr>            <chr>   <chr>           <chr>     <chr>   
##  1 0916600  COVID19  "MODERNA"        037K20A 1               IM        LA      
##  2 0916601  COVID19  "MODERNA"        025L20A 1               IM        RA      
##  3 0916602  COVID19  "PFIZER\\BIONTE… EL1284  1               IM        LA      
##  4 0916603  COVID19  "MODERNA"        unknown <NA>            <NA>      <NA>    
##  5 0916604  COVID19  "MODERNA"        <NA>    1               IM        LA      
##  6 0916606  COVID19  "MODERNA"        011J20A 1               IM        LA      
##  7 0916607  COVID19  "MODERNA"        <NA>    <NA>            IM        LA      
##  8 0916608  COVID19  "MODERNA"        <NA>    1               IM        LA      
##  9 0916609  COVID19  "MODERNA"        011J20… 1               IM        LA      
## 10 0916610  COVID19  "MODERNA"        <NA>    1               SYR       LA      
## # … with 34,620 more rows, and 1 more variable: VAX_NAME <chr>

SYMPTOMS.CSV: Contains information about the symptoms experienced after vaccination

## # A tibble: 48,110 x 11
##   VAERS_ID SYMPTOM1     SYMPTOMVERSION1 SYMPTOM2     SYMPTOMVERSION2 SYMPTOM3   
##   <chr>    <chr>                  <dbl> <chr>                  <dbl> <chr>      
## 1 0916600  Dysphagia               23.1 Epiglottitis            23.1 <NA>       
## 2 0916601  Anxiety                 23.1 Dyspnoea                23.1 <NA>       
## 3 0916602  Chest disco…            23.1 Dysphagia               23.1 Pain in ex…
## 4 0916603  Dizziness               23.1 Fatigue                 23.1 Mobility d…
## 5 0916604  Injection s…            23.1 Injection s…            23.1 Injection …
## 6 0916606  Pharyngeal …            23.1 <NA>                    NA   <NA>       
## # … with 48,104 more rows, and 5 more variables: SYMPTOMVERSION3 <dbl>,
## #   SYMPTOM4 <chr>, SYMPTOMVERSION4 <dbl>, SYMPTOM5 <chr>,
## #   SYMPTOMVERSION5 <dbl>

The aim of this project is to gain insight on the adverse effects of different Covid-19 vaccines and answer questions such as:

• Do some vaccines cause more/different symptoms than others?

• Do patients with some profiles get more/different symptoms?

• Are certain symptoms correlated with death?

• Is patient profile correlated with death?

• Does taking anti-inflammatory drugs reduce the chance of having symptoms?

Cleaning

• NAs that should be interpreted as “no” → replace_na(ALLERGIES = “N”)
• Duplicated IDs → add_count(VAERS_ID) %>% filter(n == 1) %>% select(-n)

Augment

• Columns containing long string descriptions → Make tidy categorical (Y/N) variables

## # A tibble: 3 x 3
##   VAERS_ID OTHER_MEDS                     TAKES_ANTIINFLAMATORY
##   <chr>    <chr>                          <chr>                
## 1 0916983  <NA>                           N                    
## 2 0916988  Ibuprofen  PM the night before Y                    
## 3 0916996  Clobetasol, Benadryl           N

• Too many symptoms and untidy → extract top 20 occurring symptoms and turn them into tidy categorical (TRUE/FALSE) columns

## # A tibble: 3 x 2
##   SEX       n
##   <chr> <int>
## 1 F     24070
## 2 M      8514
## 3 <NA>    828

## # A tibble: 3 x 2
##   VAX_MANU            n
##   <chr>           <int>
## 1 JANSSEN          1106
## 2 MODERNA         16253
## 3 PFIZER-BIONTECH 16053

Hypothesis: two peaks corresponding to the innate and acquired immune response. Stronger innate response in younger individuals.

Observations: no noticeable differences

Observations: no noticeable differences

Hypothesis: symptoms associated to a strong immune response are most common in younger groups.

Observations: some noticeable differences, hereamongst death, which is more common in males.

Observations: Janssen causes the most common symptoms more often (NB smaller sample size)

Observations: no observable clustering.

glm(death ~ sex + age + allergic/not + ill/not + has/had covid/not, family = binomial)

Observations: Age, illness and being male are positively correlated with death (p-value < 0.05).

glm(death ~ symptoms, family = binomial)

Observations: asthenia, dyspnoea and vomiting are positively correlated with death (p-value < 0.05).

glm(each symptom ~ takes anti-inflammatory/not , family = binomial)

Observations: no significant reduction in symptoms (p-value < 0.05). Possible increase?

## # A tibble: 2 x 4
##   DIED  JANSSEN MODERNA `PFIZER-BIONTECH`
##   <chr>   <dbl>   <dbl>             <dbl>
## 1 N        1090   15281             15212
## 2 Y          16     972               841

Observations: there is a significant difference among vaccine types (p-value < 0.05).

## # A tibble: 2 x 3
##   DIED      F     M
##   <chr> <dbl> <dbl>
## 1 N     23271  7523
## 2 Y       799   991

Observations: death is significantly more common in males (p-value < 0.05).

Uneven group sizes difficults interpretation of results. Knowing this, we observed the following:

1. Most vaccine recipients experience symptoms immediately.

2. Symptoms associated to the expected immune response happen more often in younger age groups.

3. Age, illness and being male are positively correlated with death.

4. Asthenia, dyspnoea and vomiting are positively correlated with death.

5. Anti-inflammatory drugs do not reduce symptoms.

6. The proportion of death varies among vaccine types.

7. The proportion of death is higher in males.

Dataset: https://www.kaggle.com/ayushggarg/covid19-vaccine-adverse-reactions?select=2021VAERSSYMPTOMS.csv

Dataset user guide: https://vaers.hhs.gov/docs/VAERSDataUseGuide_November2020.pdf